Individuals and organizations are rapidly accumulating large collections of digital content, including text, audio, graphics, animated graphics and full-motion video. This content may be presented individually or combined in a wide variety of different forms, including documents, presentations, still photographs, commercial videos, home movies, and metadata describing one or more associated digital content files. As these collections grow in number and diversity, individuals and organizations increasingly wilt require systems and methods for retrieving the digital content from their collections.
Among the ways that commonly are used to retrieve digital content from a collection are browsing methods and text-based retrieval methods. Browsing methods involve manually scanning through the content in the collection. Browsing, however, tends to be an inefficient way to retrieve content and typically is useful only for small content collections. Text-based retrieval methods involve submitting queries to a text-based search engine that matches the query terms to textual metadata that is associated with the content. Text-based retrieval methods typically rely on the association of manual annotations to the content, which requires a significant amount of manual time and effort.
Content-based retrieval methods also have been developed for retrieving content based on the actual attributes of the content. Content-based retrieval methods involve submitting a description of the desired content to a content-based search engine, which translates the description into a query and matches the query to one or more parameters that are associated with the content. Some content-based retrieval systems support query-by-text, to which involves matching query terms to descriptive textual metadata associated with the content. Other content-based retrieval systems additionally support query-by-content, which involves interpreting a query that describes the content in terms of attributes such as color, shape, and texture, abstractions such as objects, roles, and scenes, and subjective impressions, emotions, and meanings that are assigned to the content attributes. In some content-based image retrieval approaches, low level visual features are used to group images into meaningful categories that, in turn, are used to generate indices for a database containing the images. Exemplary low level features include texture, shape, and layout. The parameters (or terms) of an image query may be used to retrieve images in the databases that have indices that match the conditions in the image query. In general, the results of automatic categorization and indexing of images improve when the features that are used to categorize and index images accurately capture the features that are of interest to the person submitting the image queries.
A primary challenge in the design of a content-based retrieval system involves identifying meaningful attributes that can be extracted from the content and used to rank the content in accordance with the degree of relevance to a particular retrieval objective.